A method is disclosed that includes obtaining one or more pieces of threat information indicative of a data set enabling positioning based on radio signals sent by a respective radio node. The data set is further indicative of one or more parameters based on which said positioning is performed and/or controlled and which is considered to be at least partially unexpected. The method also includes utilizing the one or more pieces of threat information in a positioning. In order to obtain the one or more pieces of threat information, the method includes at least one of: obtaining the one or more pieces of threat information by using an application programming interface; or obtaining the one or more pieces of spoofing information by using a push method. A corresponding apparatus, computer-readable storage medium and system are also disclosed.

Systems, apparatuses, and methods are described for a privacy blocking device configured to prevent receipt, by a listening device, of video and/or audio data until a trigger occurs. A blocker may be configured to prevent receipt of video and/or audio data by one or more microphones and/or one or more cameras of a listening device. The blocker may use the one or more microphones, the one or more cameras, and/or one or more second microphones and/or one or more second cameras to monitor for a trigger. The blocker may process the data. Upon detecting the trigger, the blocker may transmit data to the listening device. For example, the blocker may transmit all or a part of a spoken phrase to the listening device.

Provided is a method and a computer device for performing the method for defending a perimeter against a small unmanned aerial system (sUAS). The method includes detecting a presence of a wireless access point (WAP) associated with a sUAS; analyzing data packets intercepted from the WAP; determining the type of sUAS based on the data packets that were intercepted using a machine learning classifier; determining one or more exploits from a library of exploits to initiate against the sUAS based on the type of sUAS determined by the machine learning classifier; and transmitting the one or more exploits to the sUAS.

A method of detecting an operation to spoof a first positioning device carried by a first vehicle moving in a zone in which at least one second vehicle carrying a second positioning device is also moving, the method comprising the step of causing at least one first positioning value to be calculated for each vehicle from initial satellite signals received by each device; the method being characterized in that it further comprises the steps of: causing the second device to initiate a latching stage in order to make a new search for satellite signals and using the new satellite signals received by the second device to calculate a second positioning value for the second vehicle at the same instant as the first value; comparing the first and second values relating to the second vehicle; and issuing a warning when the two values do not coincide.

The system and methods described herein aids in the defense of unmanned vehicles, such as aerial vehicles, from wifi cyber attacks. Such attacks usually do not last long and in the case of many point-to-point command and control systems, the attacks originate from close proximity to the unmanned vehicle. The system and methods described herein allow a team to rapidly identify and physically respond to an adversary trying to take control of the unmanned vehicle. Another aspect of the embodiment taught herein is to allow for the location of a wifi signal in a hands-free manner by able to visualize the source of the signal using an augmented reality display coupled to an antenna array.

Systems, methods, and apparatus for identifying, tracking, and disrupting UAVs are described herein. A tracking system can include one or more first computing devices that receive sensor data associated with an object in a particular airspace from one or more sensors. The first computing device can analyze the sensor data relating to the object to determine information about the object. A portable countermeasure device can include one or more second computing devices. The second computing device can receive the information relating to the object. The second computing device can display a visual indicator indicating the information on a display.

A device and method for anti jamming using a decoy signal are provided. The device may include a processor configured to allocate, in connection with a request for transmission of information, first frequency band to information signal including the information and allocate a second frequency band to the decoy signal including fake information associated with the information, and an outputter configured to output the decoy signal and the information signal at a predetermined time interval and output the information signal through the first frequency band after an attack on the second frequency band by the jammer that inspects the decoy signal is detected.

A method for processing information related to one or more monitored areas, the method may include receiving by a central control unit, UAV indicators about at least one unmanned airborne vehicle (UAV) located within the one or more monitored areas; wherein the UAV indicators are generated by a preprocessing step that comprises extracting information that is embedded within sensed UAV communication, the sensed UAV communication is sensed by at least one sensor of a group of sensors; aggregating, by a central control unit, the UAV indicators, to provide aggregated information; and responding to the aggregated information, wherein the responding comprises at least one out of (i) outputting the aggregated information, and (ii) storing the aggregated information.

A null steering adjuster in a stationary wireless network identifies the presence or absence of a current set of phase differences in a dataset. The dataset includes legitimate sets of phase differences detected between radio frequency signals received by multiple antennas from respective legitimate sources. The current set of phase differences is detected between radio frequency signals currently received by the antennas. When the current set of phase differences is absent from the dataset, a null is created in the antenna pattern of the antennas in the direction of the currently-received radio frequency signals. When the current set of phase differences is present in the dataset, the antenna pattern is maintained.

A system for providing integrated detection and deterrence against an unmanned vehicle including but not limited to aerial technology unmanned systems using a detection element, a tracking element, an identification element and an interdiction or deterrent element. Elements contain sensors that observe real time quantifiable data regarding the object of interest to create an assessment of risk or threat to a protected area of interest. This assessment may be based e.g., on data mining of internal and external data sources. The deterrent element selects from a variable menu of possible deterrent actions. Though designed for autonomous action, a Human in the Loop may override the automated system solutions.